function [SDR,ISR,SIR,SAR,perm]=bss_eval_images(ie,i) % BSS_EVAL_IMAGES Ordering and measurement of the separation quality for % estimated source spatial image signals in terms of true source, spatial % (or filtering) distortion, interference and artifacts. % % [SDR,ISR,SIR,SAR,perm]=bss_eval_images(ie,i) % % Inputs: % ie: nsrc x nsampl x nchan matrix containing estimated source images % i: nsrc x nsampl x nchan matrix containing true source images % % Outputs: % SDR: nsrc x 1 vector of Signal to Distortion Ratios % ISR: nsrc x 1 vector of source Image to Spatial distortion Ratios % SIR: nsrc x 1 vector of Source to Interference Ratios % SAR: nsrc x 1 vector of Sources to Artifacts Ratios % perm: nsrc x 1 vector containing the best ordering of estimated source % images in the mean SIR sense (estimated source image number perm(j) % corresponds to true source image number j) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Copyright 2007-2008 Emmanuel Vincent % This software is distributed under the terms of the GNU Public License % version 3 (http://www.gnu.org/licenses/gpl.txt) % If you find it useful, please cite the following reference: % Emmanuel Vincent, Hiroshi Sawada, Pau Bofill, Shoji Makino and Justinian % P. Rosca, "First stereo audio source separation evaluation campaign: % data, algorithms and results," In Proc. Int. Conf. on Independent % Component Analysis and Blind Source Separation (ICA), pp. 552-559, 2007. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Errors %%% if nargin<2, error('Not enough input arguments.'); end [nsrc,nsampl,nchan]=size(ie); [nsrc2,nsampl2,nchan2]=size(i); if nsrc2~=nsrc, error('The number of estimated source images and reference source images must be equal.'); end if nsampl2~=nsampl, error('The estimated source images and reference source images must have the same duration.'); end if nchan2~=nchan, error('The estimated source images and reference source images must have the same number of channels.'); end %%% Performance criteria %%% % Computation of the criteria for all possible pair matches SDR=zeros(nsrc,nsrc); ISR=zeros(nsrc,nsrc); SIR=zeros(nsrc,nsrc); SAR=zeros(nsrc,nsrc); for jest=1:nsrc, for jtrue=1:nsrc, [s_true,e_spat,e_interf,e_artif]=bss_decomp_mtifilt(reshape(ie(jest,:,:),nsampl,nchan).',i,jtrue,512); [SDR(jest,jtrue),ISR(jest,jtrue),SIR(jest,jtrue),SAR(jest,jtrue)]=bss_image_crit(s_true,e_spat,e_interf,e_artif); end end % Selection of the best ordering perm=perms(1:nsrc); nperm=size(perm,1); meanSIR=zeros(nperm,1); for p=1:nperm, meanSIR(p)=mean(SIR((0:nsrc-1)*nsrc+perm(p,:))); end [meanSIR,popt]=max(meanSIR); perm=perm(popt,:).'; SDR=SDR((0:nsrc-1).'*nsrc+perm); ISR=ISR((0:nsrc-1).'*nsrc+perm); SIR=SIR((0:nsrc-1).'*nsrc+perm); SAR=SAR((0:nsrc-1).'*nsrc+perm); return; function [s_true,e_spat,e_interf,e_artif]=bss_decomp_mtifilt(se,s,j,flen) % BSS_DECOMP_MTIFILT Decomposition of an estimated source image into four % components representing respectively the true source image, spatial (or % filtering) distortion, interference and artifacts, derived from the true % source images using multichannel time-invariant filters. % % [s_true,e_spat,e_interf,e_artif]=bss_decomp_mtifilt(se,s,j,flen) % % Inputs: % se: nchan x nsampl matrix containing the estimated source image (one row per channel) % s: nsrc x nsampl x nchan matrix containing the true source images % j: source index corresponding to the estimated source image in s % flen: length of the multichannel time-invariant filters in samples % % Outputs: % s_true: nchan x nsampl matrix containing the true source image (one row per channel) % e_spat: nchan x nsampl matrix containing the spatial (or filtering) distortion component % e_interf: nchan x nsampl matrix containing the interference component % e_artif: nchan x nsampl matrix containing the artifacts component %%% Errors %%% if nargin<4, error('Not enough input arguments.'); end [nchan2,nsampl2]=size(se); [nsrc,nsampl,nchan]=size(s); if nchan2~=nchan, error('The number of channels of the true source images and the estimated source image must be equal.'); end if nsampl2~=nsampl, error('The duration of the true source images and the estimated source image must be equal.'); end %%% Decomposition %%% % True source image s_true=[reshape(s(j,:,:),nsampl,nchan).',zeros(nchan,flen-1)]; % Spatial (or filtering) distortion e_spat=project(se,s(j,:,:),flen)-s_true; % Interference e_interf=project(se,s,flen)-s_true-e_spat; % Artifacts e_artif=[se,zeros(nchan,flen-1)]-s_true-e_spat-e_interf; return; function sproj=project(se,s,flen) % SPROJ Least-squares projection of each channel of se on the subspace % spanned by delayed versions of the channels of s, with delays between 0 % and flen-1 [nsrc,nsampl,nchan]=size(s); s=reshape(permute(s,[3 1 2]),nchan*nsrc,nsampl); %%% Computing coefficients of least squares problem via FFT %%% % Zero padding and FFT of input data s=[s,zeros(nchan*nsrc,flen-1)]; se=[se,zeros(nchan,flen-1)]; fftlen=2^nextpow2(nsampl+flen-1); sf=fft(s,fftlen,2); sef=fft(se,fftlen,2); % Inner products between delayed versions of s G=zeros(nchan*nsrc*flen); for k1=0:nchan*nsrc-1, for k2=0:k1, ssf=sf(k1+1,:).*conj(sf(k2+1,:)); ssf=real(ifft(ssf)); ss=toeplitz(ssf([1 fftlen:-1:fftlen-flen+2]),ssf(1:flen)); G(k1*flen+1:k1*flen+flen,k2*flen+1:k2*flen+flen)=ss; G(k2*flen+1:k2*flen+flen,k1*flen+1:k1*flen+flen)=ss.'; end end % Inner products between se and delayed versions of s D=zeros(nchan*nsrc*flen,nchan); for k=0:nchan*nsrc-1, for i=1:nchan, ssef=sf(k+1,:).*conj(sef(i,:)); ssef=real(ifft(ssef,[],2)); D(k*flen+1:k*flen+flen,i)=ssef(:,[1 fftlen:-1:fftlen-flen+2]).'; end end %%% Computing projection %%% % Distortion filters C=G\D; C=reshape(C,flen,nchan*nsrc,nchan); % Filtering sproj=zeros(nchan,nsampl+flen-1); for k=1:nchan*nsrc, for i=1:nchan, sproj(i,:)=sproj(i,:)+fftfilt(C(:,k,i).',s(k,:)); end end return; function [SDR,ISR,SIR,SAR]=bss_image_crit(s_true,e_spat,e_interf,e_artif) % BSS_IMAGE_CRIT Measurement of the separation quality for a given source % image in terms of true source, spatial (or filtering) distortion, % interference and artifacts. % % [SDR,ISR,SIR,SAR]=bss_image_crit(s_true,e_spat,e_interf,e_artif) % % Inputs: % s_true: nchan x nsampl matrix containing the true source image (one row per channel) % e_spat: nchan x nsampl matrix containing the spatial (or filtering) distortion component % e_interf: nchan x nsampl matrix containing the interference component % e_artif: nchan x nsampl matrix containing the artifacts component % % Outputs: % SDR: Signal to Distortion Ratio % ISR: source Image to Spatial distortion Ratio % SIR: Source to Interference Ratio % SAR: Sources to Artifacts Ratio %%% Errors %%% if nargin<4, error('Not enough input arguments.'); end [nchant,nsamplt]=size(s_true); [nchans,nsampls]=size(e_spat); [nchani,nsampli]=size(e_interf); [nchana,nsampla]=size(e_artif); if ~((nchant==nchans)&&(nchant==nchani)&&(nchant==nchana)), error('All the components must have the same number of channels.'); end if ~((nsamplt==nsampls)&&(nsamplt==nsampli)&&(nsamplt==nsampla)), error('All the components must have the same duration.'); end %%% Energy ratios %%% % SDR SDR=10*log10(sum(sum(s_true.^2))/sum(sum((e_spat+e_interf+e_artif).^2))); % ISR ISR=10*log10(sum(sum(s_true.^2))/sum(sum(e_spat.^2))); % SIR SIR=10*log10(sum(sum((s_true+e_spat).^2))/sum(sum(e_interf.^2))); % SAR SAR=10*log10(sum(sum((s_true+e_spat+e_interf).^2))/sum(sum(e_artif.^2))); return;